IDEAS home Printed from https://ideas.repec.org/a/eee/telpol/v44y2020i4s0308596119301429.html
   My bibliography  Save this article

IPTV vs. emerging video services: Dilemma of telcos to upgrade the broadband

Author

Listed:
  • Kim, Jiwhan
  • Nam, Changi
  • Ryu, Min Ho

Abstract

IPTV is an important tool to change business structures and move beyond subscription-based business models for telecom operators. However, the level of IPTV penetration differs among operators, which might be closely related to individual operator's strategy for the broadband market and the regulatory environment. Controlling country-specific business environments, this study identifies the key factors influencing IPTV penetration rates. Results show that broadband penetration, broadband quality, telecommunications service fee, and broadband cap are important factors leading to greater IPTV penetration. This might provide valuable suggestions to telecom operators, such as strategies for leveraging broadband quality and data cap to compete against emerging video services, or bundling strategies with price benefits to convert more broadband users into IPTV subscribers. Comparison of groups differing in IPTV penetration rates, GDP per capital, and percentage of urban population are conducted to gain additional insight into the contextual differences between countries. The results reinforce the importance of constructing high quality broadband infrastructure and taking advantage of bundling plans.

Suggested Citation

  • Kim, Jiwhan & Nam, Changi & Ryu, Min Ho, 2020. "IPTV vs. emerging video services: Dilemma of telcos to upgrade the broadband," Telecommunications Policy, Elsevier, vol. 44(4).
  • Handle: RePEc:eee:telpol:v:44:y:2020:i:4:s0308596119301429
    DOI: 10.1016/j.telpol.2019.101889
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308596119301429
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.telpol.2019.101889?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yeong-Wha Sawng & Kazuyuki Motohashi & Gang-Hoon Kim, 2013. "Comparative analysis of innovative diffusion in the high-tech markets of Japan and South Korea: a use–diffusion model approach," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 143-166, March.
    2. Donald Rubin & Dorothy Thayer, 1982. "EM algorithms for ML factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 47(1), pages 69-76, March.
    3. Kim, Juran & Lee, Ki Hoon, 2013. "Towards a theoretical framework of motivations and interactivity for using IPTV," Journal of Business Research, Elsevier, vol. 66(2), pages 260-264.
    4. Sung, Nakil & Kwack, Eunkyoung, 2016. "IPTV's videos on demand for television programs, their usage patterns, and inter-channel relationship in Korea," Telecommunications Policy, Elsevier, vol. 40(10), pages 1064-1076.
    5. Lee, Misuk & Cho, Youngsang, 2015. "Consumer perception of a new convergence product: A theoretical and empirical approach," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 312-321.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hadjielias, Elias & (Lola) Dada, Olufunmilola & Discua Cruz, Allan & Zekas, Stavros & Christofi, Michael & Sakka, Georgia, 2021. "How do digital innovation teams function? Understanding the team cognition-process nexus within the context of digital transformation," Journal of Business Research, Elsevier, vol. 122(C), pages 373-386.
    2. Park, Sungwook & Kwon, Youngsun, 2023. "Disentangling the effects on OTT platform performance of three strategies: Pricing, M&As, and content investments," Telecommunications Policy, Elsevier, vol. 47(8).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    2. Chen, Derek H. C. & Gawande, Kishore, 2007. "Underlying dimensions of knowledge assessment : factor analysis of the knowledge assessment methodology data," Policy Research Working Paper Series 4216, The World Bank.
    3. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    4. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Jun 2024.
    5. Gregory Camilli & Jean-Paul Fox, 2015. "An Aggregate IRT Procedure for Exploratory Factor Analysis," Journal of Educational and Behavioral Statistics, , vol. 40(4), pages 377-401, August.
    6. Sentana, Enrique, 2004. "Factor representing portfolios in large asset markets," Journal of Econometrics, Elsevier, vol. 119(2), pages 257-289, April.
    7. Fiorentini, Gabriele & Galesi, Alessandro & Sentana, Enrique, 2018. "A spectral EM algorithm for dynamic factor models," Journal of Econometrics, Elsevier, vol. 205(1), pages 249-279.
    8. Bai, Jushan, 2024. "Likelihood approach to dynamic panel models with interactive effects," Journal of Econometrics, Elsevier, vol. 240(1).
    9. Kim, Juran & Lee, Ki Hoon, 2019. "Influence of integration on interactivity in social media luxury brand communities," Journal of Business Research, Elsevier, vol. 99(C), pages 422-429.
    10. Keiji Takai, 2012. "Constrained EM algorithm with projection method," Computational Statistics, Springer, vol. 27(4), pages 701-714, December.
    11. Zhuo Chen & Gregory Connor & Robert A Korajczyk, 2018. "A Performance Comparison of Large-n Factor Estimators," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 8(1), pages 153-182.
    12. Xiaoping Zhou & Dmitry Malioutov & Frank J. Fabozzi & Svetlozar T. Rachev, 2014. "Smooth monotone covariance for elliptical distributions and applications in finance," Quantitative Finance, Taylor & Francis Journals, vol. 14(9), pages 1555-1571, September.
    13. Dumas, Bernard & Gabuniya, Tymur & Marston, Richard C., 2022. "Firms’ exposures to geographic risks," Journal of International Money and Finance, Elsevier, vol. 122(C).
    14. Nikolaos Zirogiannis & Yorghos Tripodis, 2013. "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Papers 2013-1, University of Massachusetts Amherst, Department of Resource Economics.
    15. Sundberg, Rolf & Feldmann, Uwe, 2016. "Exploratory factor analysis—Parameter estimation and scores prediction with high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 49-59.
    16. Alev Kocak Alan & Ebru Tumer Kabadayi & Cengiz Yilmaz, 2016. "Cognitive and affective constituents of the consumption experience in retail service settings: effects on store loyalty," Service Business, Springer;Pan-Pacific Business Association, vol. 10(4), pages 715-735, December.
    17. Wagenvoort, Rien J.L.M. & Ebner, André & Morgese Borys, Magdalena, 2011. "A factor analysis approach to measuring European loan and bond market integration," Journal of Banking & Finance, Elsevier, vol. 35(4), pages 1011-1025, April.
    18. Donald Rubin & Dorothy Thayer, 1983. "More on EM for ML factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 253-257, June.
    19. Zhao, Jianhua & Shi, Lei, 2014. "Automated learning of factor analysis with complete and incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 205-218.
    20. Jonathan James, 2018. "Estimation of Factor Structured Covariance Mixed Logit Models," Working Papers 1802, California Polytechnic State University, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:telpol:v:44:y:2020:i:4:s0308596119301429. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30471/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.